An Empirical Study on Fake News Detection System using Deep and Machine Learning Ensemble Techniques
International Journal of Advanced Computer Science and Applications
With the revolution that happened in electronic gadgets in the past few years, information sharing has evolved into a new era that can spread the news globally in a fraction of minutes, either through yellow media or through satellite communication without any proper authentication. At the same time, all of us are aware that with the increase of different social media platforms, many organizations try to grab people's attention by creating fake news about celebrities, politicians (or) politics,
... branded products, and others. There are three ways to generate fake news: tampering with an image using advanced morphing tools; this is generally a popular technique while posting phony information about the celebrities (or) cybercrimes related to women. The second one deals with the reposting of the old happenings with new fake content injected into it. For example, in generally few social media platforms either to increase their TRP ratings or to expand their subscribers, they create old news that happened somewhere years ago as latest one with new fake content like by changing the date, time, locations, and other important information and tries to make them viral across the globe. The third one deals with the image/video real happened at an event or place, but media try to change the content with a false claim instead of the original one that occurred. A few decades back, researchers started working on fake news detection topics with the help of textual data. In the recent era, few researchers worked on images and text data using traditional and ensemble deep and machine learning algorithms, but they either suffer from overfitting problems due to insufficient data or unable to extract the complex semantic relations between documents. The proposed system designs a transfer learning environment where Neural Style Transfer Learning takes care of the size and quality of the datasets. It also enhances the auto-encoders by customizing the hidden layers to handle complex problems in the real world.